EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease

Sonja Simpraga, Ricardo Alvarez-Jimenez, Huibert D. Mansvelder, Joop M.A. Van Gerven, Geert Jan Groeneveld, Simon Shlomo Poil, Klaus Linkenkaer-Hansen*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electroencephalography (EEG) recordings to capture the brain's multi-faceted signature of disease or pharmacological intervention and use machine learning to improve classification performance. Using data from healthy subjects receiving scopolamine we developed an index of the muscarinic acetylcholine receptor antagonist (mAChR) consisting of 14 EEG biomarkers. This mAChR index yielded higher classification performance than any single EEG biomarker with cross-validated accuracy, sensitivity, specificity and precision ranging from 88-92%. The mAChR index also discriminated healthy elderly from patients with Alzheimer's disease (AD); however, an index optimized for AD pathophysiology provided a better classification. We conclude that integrating multiple EEG biomarkers can enhance the accuracy of identifying disease or drug interventions, which is essential for clinical trials.

Original languageEnglish
Article number5775
JournalScientific Reports
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Dec 2017

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